agramfort

import numpy as np

from sklearn.datasets import fetch_covtype
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Imputer
from sklearn.model_selection import cross_val_score

rng = np.random.RandomState(0)

agramfort

#%% import ####################################################################
import mne
import numpy as np
import os

from mne.datasets import sample
data_path = sample.data_path()

#%% load group avg data #######################################################
os.environ["SUBJECTS_DIR"] = data_path + '/subjects'

agramfort

import numpy as np
from scipy import linalg
from sklearn import linear_model

def _gcv(X, y, alphas, fit_intercept):
    """Local gcv"""
    # singular values of the design matrix
    n1, p = X.shape
    _, s, _ = linalg.svd(X, 0)
    clf = linear_model.Ridge(fit_intercept=fit_intercept)

agramfort

# -*- coding: utf-8 -*-
"""
Generalized linear models via coordinate descent

Author: Fabian Pedregosa <[email protected]>
"""

import numpy as np

MAX_ITER = 100

agramfort

import numpy as np


def fsign( f):
    if f == 0:
        return 0
    elif f > 0:
        return 1.0
    else:
        return -1.0

agramfort

#!/usr/bin/python
# -*- coding: utf-8 -*-

"""
=========================================================
Title
=========================================================
Description

"""

agramfort

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer.

Authors: Fabian Pedregosa <[email protected]>
         Alexandre Gramfort <[email protected]>
"""

agramfort

"""
Author: Oliver Mitevski

References:
A Generalized Linear Model for Principal Component Analysis of Binary Data,
Andrew I. Schein; Lawrence K. Saul; Lyle H. Ungar

The code was translated and adapted from Jakob Verbeek's
"Hidden Markov models and mixtures for Binary PCA" implementation in MATLAB